The development trend of wireless communication systems is toward higher data rates to satisfy ever increasing demands for wireless data traffic. For example, wireless communication systems are being developed toward increased spectral efficiency based on communication schemes such as Orthogonal Frequency Division Multiple Access (OFDMA), Multiple Input Multiple Output (MIMO), and the like in order to increase data rates.
OFDMA is a multi-user version of the popular orthogonal frequency-division multiplexing (OFDM) digital modulation scheme. Multiple access is achieved in OFDMA by assigning subsets of subcarriers to individual users. This allows simultaneous low data rate transmission from several users.
As demands for traffic have accelerated due to increased demands for smartphones and tablets and the resulting rapid growth of applications requiring a large amount of traffic, it is difficult to satisfy the increasing demands for wireless data simply by increasing spectral efficiency. Recent interest has focused on a millimeter-wave (mmWave) wireless communication system.
Communication over mmWave frequencies may be the frontier for commercial wireless communication systems. Initial applications of mmWave to personal area networks (PAN) and local area networks (LAN) through the 60 GHz unlicensed band are already standardized and commercially available. The large bandwidths available at mmWave carrier frequencies also make it appealing for 5G cellular systems.
MmWave communication requires very large MIMO systems to provide sufficient antenna aperture. Unfortunately, at mmWave there are additional hardware constraints that have to imposed due to the practical limitations on the cost, complexity and power consumption with the current technology. Due to mixed signal and baseband processing requirements, it may not be feasible to use one complete RF chain and one DAC or ADC per antenna, so precoding and combining can not be done entirely in the baseband. For this reason, systems like IEEE 802.11ad use analog beamforming/combining and only support single stream MIMO communication. Generalizing to larger numbers of streams requires the use of precoding and combining, making functions like low complexity and low overhead channel estimation more essential.
In previous work, a hybrid architecture that accounts for hardware constraints has been proposed resulting in precoder/combiner design algorithms that divide the optimization process into the RF and the baseband stages, and channel estimation methods that exploit the sparse nature of the mmWave channel. This architecture is based on quantized phase shifters.
When wireless communication is provided in a mmWave frequency band, propagation loss, such as path loss and reflection loss, is increased in view of the spectral nature of the mmWave frequency band and the resulting shortened propagation distance reduces service coverage. Therefore, the mmWave wireless communication system may expand service coverage by mitigating the path loss of waves using beamforming and thus increasing the propagation distance of the waves.
The two types of beamforming schemes are digital beamforming (or Transmit (Tx) pre-Inverse Fast Fourier Transform (pre-IFFT) beamforming/Receive (Rx) post-Fast Fourier Transform (post-FFT) beamforming) and analog beamforming (or Tx post-IFFT beamforming/Rx pre-FFT beamforming). Digital beamforming uses a plurality of Radio Frequency (RF) paths based on Multiple Input Multiple Output (MIMO) and a digital precoder or codebook in the digital domain, and analog beamforming uses a plurality of analog/RF devices (e.g., a phase shifter, a Power Amplifier (PA), and a Variable Gain Amplifier (VGA)) and an antenna configuration. While digital beamforming requires an expensive Digital to Analog Converter (DAC) or Analog to Digital Converter (ADC) and increases implementation complexity in order to increase a beamforming gain, analog beamforming has limitations in terms of efficient use of frequency resources or maximization of beamforming performance.
Since a wavelength is shortened in a mmWave band, analog beamforming using an antenna array with a number of antenna elements arranged in a small space, such as a Uniform Linear Array (ULA) or a Uniform Planar Array (UPA), is suitable. However, the analog beamforming has limitations in its effectiveness in terms of efficient use of resources, the increase of user or system throughput through MIMO schemes such as Single User MIMO (SU-MIMO), Multiple User MIMO (MU-MIMO), or spatial multiplexing, and the increase of Signal to Noise Ratio (SNR) or reliability through diversity or additional digital beamforming, as described before.
For these reasons, beamforming design with a reduced number of RF chains is attractive. One approach FOR achieving this reduction is to deploy beamforming at both the digital (or frequency) domain and the analog (or time) domain, i.e., hybrid beamforming (HBF). In the digital domain, beamforming can be realized using microprocessors whereas, in the analog domain, beamforming may be implemented by using low cost phase shifters.
Due to the architecture of hybrid beamforming, the signals from each antenna element are not available in baseband unit, which leads to the difficulty of beam training since beam training algorithms are typically conducted in baseband. The beam training approaches may be quite different and should be carefully designed for training the optimal transmit Tx beams and receive Rx beams.
The combination of OFDMA and hybrid beamforming include a difficulty in that the analog beamformer in the transmitter and the analog combiner in the receiver would be applied in the time domain which is very different from the conventional OFDMA architecture (such as LTE/LTE-A). An OFDMA-based LTE/LTE-A system applies digital precoding in baseband and the analog elements are not involved in energy-directive conducting. So, to receive the signal correctly, the analog beamformer in the transmitter and the analog combiner in the receiver need to be adjusted prior to the data transmission.
So, a practical beam training approach for channel estimation in OFDMA-based HBF systems may be needed.